Abstract
Word sense disambiguation is crucial in natural language processing. Both unsupervised knowledge-based and supervised methodologies try to disambiguate ambiguous words through context. However, they both suffer from data sparsity, a common problem in natural language. Furthermore, the supervised methods are previously limited in the all-word WSD tasks. This paper attempts to collect all publicly available contexts to enrich the ambiguous word’s sense representation and apply these contexts to the simplified Lesk and our M-IMS systems. Evaluations performed on the concatenation of several benchmark fine-grained all-word WSD datasets show that the simplified Lesk improves by 9.4% significantly and our M-IMS has shown some improvement as well.
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Acknowledgements
This work was partially supported by the National Natural Science Foundation of China (61772288), and the Natural Science Foundation of Tianjin City (18JCZDJC30900).
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Liu, Yf., Wei, J. (2019). Word Sense Disambiguation with Massive Contextual Texts. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11448. Springer, Cham. https://doi.org/10.1007/978-3-030-18590-9_60
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DOI: https://doi.org/10.1007/978-3-030-18590-9_60
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